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Use SQLAlchemy ORMs to Access LinkedIn Data in Python

The CData Python Connector for LinkedIn enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of LinkedIn data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for LinkedIn and the SQLAlchemy toolkit, you can build LinkedIn-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to LinkedIn data to query, update, delete, and insert LinkedIn data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live LinkedIn data in Python. When you issue complex SQL queries from LinkedIn, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to LinkedIn and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to LinkedIn Data

Connecting to LinkedIn data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

LinkedIn uses the OAuth 2 authentication standard. You will need to obtain the OAuthClientId and OAuthClientSecret by registering an app with LinkedIn. For more information refer to our authentication guide.

Follow the procedure below to install SQLAlchemy and start accessing LinkedIn through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model LinkedIn Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with LinkedIn data.

engine = create_engine("linkedin///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for LinkedIn Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the CompanyStatusUpdates table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class CompanyStatusUpdates(base):
	__tablename__ = "CompanyStatusUpdates"
	VisibilityCode = Column(String,primary_key=True)
	Comment = Column(String)
	...

Query LinkedIn Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("linkedin///?OAuthClientId=MyOAuthClientId&OAuthClientSecret=MyOAuthClientSecret&CallbackURL=http://localhost:portNumber&CompanyId=XXXXXXXInitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(CompanyStatusUpdates).filter_by(EntityId="238"):
	print("VisibilityCode: ", instance.VisibilityCode)
	print("Comment: ", instance.Comment)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

CompanyStatusUpdates_table = CompanyStatusUpdates.metadata.tables["CompanyStatusUpdates"]
for instance in session.execute(CompanyStatusUpdates_table.select().where(CompanyStatusUpdates_table.c.EntityId == "238")):
	print("VisibilityCode: ", instance.VisibilityCode)
	print("Comment: ", instance.Comment)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert LinkedIn Data

To insert LinkedIn data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to LinkedIn.

new_rec = CompanyStatusUpdates(VisibilityCode="placeholder", EntityId="238")
session.add(new_rec)
session.commit()

Update LinkedIn Data

To update LinkedIn data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to LinkedIn.

updated_rec = session.query(CompanyStatusUpdates).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.EntityId = "238"
session.commit()

Delete LinkedIn Data

To delete LinkedIn data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided recoreds (rows).

deleted_rec = session.query(CompanyStatusUpdates).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

Download a free, 30-day trial of the LinkedIn Python Connector to start building Python apps and scripts with connectivity to LinkedIn data. Reach out to our Support Team if you have any questions.